Systems and Methods for Downlink Power Control and Scheduling in a Wireless Network

ABSTRACT

Methods and systems for providing joint power control (PC) and scheduling in a wireless network are provided. In one example, a method includes generating a near-optimal power pattern for PC and scheduling in accordance with long term channel statistics. The near-optimal PC solution may be generated by first generating a set of possible power patterns in accordance with likely scheduling scenarios, then statistically narrowing the set of possible power patterns to identify the most commonly used power patterns, and finally selecting one of the most commonly used power patterns as the near-optimal power pattern. In another example, a table of optimal PC solutions are provided for performing distributed PC and scheduling in an adaptive and/or dynamic manner.

TECHNICAL FIELD

The present invention relates generally to wireless communications, and,in particular embodiments, to optimizing downlink power control andscheduling in wireless communication systems.

BACKGROUND

Downlink power control (PC) is an important consideration in modern daycellular communication systems that rely on code division multipleaccess (CDMA) schemes, Orthogonal Frequency-Division Multiple Access(OFDMA) schemes, single carrier frequency division multiple access(SC-FDMA) schemes, and others, to manage downlink communications.Specifically (long term evolution (LTE) wireless networks), downlink PCregulates the power levels of frequency bands and the assignment oftime-frequency resources in the Physical Downlink Shared Channel(PDSCH). Effective downlink PC schemes will provide adequate coverageand throughput in a wireless network.

SUMMARY OF THE INVENTION

Technical advantages are generally achieved, by preferred embodiments ofthe present invention which describe system and methods for optimizingdownlink power control.

In accordance with an embodiment, a method for providing joint powercontrol (PC) and scheduling in a wireless network is provided. In thisexample, the method includes receiving long term channel statisticscollected during a first instance of a periodic time interval, andgenerating a near-optimal power pattern based on the long term channelstatistics. In an embodiment, the near-optimal power pattern may beobtained by first generating a set of possible power patterns inaccordance with likely scheduling scenarios, then statisticallynarrowing the set of possible power patterns to identify the mostcommonly used power patterns, and finally selecting one of the mostcommonly used power patterns as the near-optimal power pattern. Inaccordance with another embodiment, an apparatus for performing thismethod is provided.

In accordance with yet another embodiment, a method for facilitatingdynamic adaptive fractional frequency reuse (FFR) in a wireless network.In this example, the method includes identifying a plurality of commonscheduling scenarios observed during an extended period in a wirelessnetwork, and generating a table of optimal power patterns for the commonscheduling scenarios. The optimal power patterns may be determined inaccordance with the long term channel statistics, and the table ofoptimal power patterns may be provided to distributed base stations forimplementation in the wireless network. In accordance with yet otherembodiments, appropriate apparatuses (e.g., a central controller, eNBs,etc.) for implementing this method are provided.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1 illustrates a wireless network architecture for implementing adistributed power control scheme;

FIG. 2 illustrates a diagram of a local PC solution;

FIG. 3 illustrates a wireless network architecture for implementing astatic fractional frequency re-use (FFR) power control scheme;

FIG. 4 illustrates a diagram of a wireless network architecture forimplementing centralized or hybrid power control scheme;

FIG. 5 illustrates a diagram of a global PC solution;

FIG. 6( a) illustrates a diagram of a hybrid PC including joint powercontrol (JPC) with localized scheduling;

FIG. 6( b) illustrates a diagram of a JPC pattern including powersetting vectors;

FIG. 7 illustrates a diagram of a communication protocol for performinghybrid power control including JPC with localized scheduling;

FIG. 8 illustrates an embodiment of a method for computing powerpatterns for use in semi-static JPC with localized scheduling;

FIG. 9 illustrates a diagram of an embodiment of a recurring sequence ofintervals for semi-static JPC;

FIG. 11 illustrates an embodiment of semi-static JPC using a singlepower pattern;

FIG. 12 illustrates an embodiment of semi-static JPC using multiplepower patterns;

FIG. 13 illustrates a flowchart of an embodiment of a method forgenerating an adaptive power pattern table;

FIG. 14 illustrates a flowchart of an embodiment of a method forperforming adaptive PC in a wireless network;

FIG. 15 illustrates a graph depicting variations in performance fordifferent power control schemes;

FIG. 16 illustrates a block diagram of an embodiment base station; and

FIG. 17 illustrates a block diagram of an embodiment PC controller.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. The figures aredrawn to clearly illustrate the relevant aspects of the preferredembodiments and are not necessarily drawn to scale.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The making and using of the presently preferred embodiments arediscussed in detail below. It should be appreciated, however, that thepresent invention provides many applicable inventive concepts that canbe embodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

Downlink PC can be managed in a distributed manner such that downlink PCsolutions are computed locally by base stations (eNBs). FIG. 1illustrates a network 100 for performing localized/distributed PC wherea plurality of neighboring eNBs 110-130 compute their PC solutionsindependently. As shown, the eNB 110 has a coverage area 112, the eNB120 has a coverage area 122, and the eNB 130 has a coverage area 132(user equipments (UEs) have been omitted for the sake of clarity andconcision). FIG. 2 illustrates a PC solution 200 computed locally by theeNB 110 for a block of available time-frequency resources, which arerepresented by resource blocks (RBs) (denoted by the squares) extendingover a time domain (T) (including timeslots T1, T2, . . . , TN) and afrequency spectrum (F) (including frequency bands f₁, f₂, . . . f₁₆).Notably, the frequency spectrum (F) is exemplary and may be modified toinclude more or fewer than sixteen frequency bands based on the needs ofthe system. The PC solution 200 includes a plurality of power settings(p₁-p₁₆) and a plurality of scheduling assignment (s₁-s₁₆) for the RBsof a given timeslot (T1, T2, . . . TN). Each power setting (p_(i)) mayspecify a power level to be used for a downlink transmission in thecorresponding RB, while each scheduling assignment (s_(i)) may specify amember UE that has been assigned to receive the downlink transmission inthe corresponding RB. In some embodiments, one or more of the schedulingassignments may specify multiple UEs, for instance, when an RB isdesignated for multi-user multiple-input-multiple-output (MU-MIMO)applications.

Although scalable, high levels of inter-cell-interference often resultbetween neighboring cells when downlink PC is managed withoutcentralized management or joint cooperation amongst neighboring eNBs.Specifically, eNBs typically transmit downlink signals at higher powerlevels for frequencies assigned to cell-edge users (CEUs) than forfrequencies assigned to cell center users (CCUs) to compensate forgreater signal attenuation attributable to the longer propagationdistance. As such, high levels of inter-cell-interference (ICI)generally result when neighboring cells use the same frequency band tocommunicate with their respective CEUs, as may often occur when PC ishandled independently by each eNB.

One alternative to localized PC is to statically assign differentfrequencies for use at the cell-edge of neighboring cells, which maygenerally be referred to as fractional frequency reuse (FFR). FIG. 3illustrates a diagram of a network 300 for implementing a static FFRscheme. As shown, the network 300 comprises a plurality of neighboringeNBs 310-320 (user equipments (UEs) are omitted for the sake of clarityand concision). The eNB 310 has a coverage area 312, which is dividedinto a center cell area 314 and a cell edge area 316. Similarly, the eNB320 has a coverage area 322 (divided into a center cell area 324 and acell edge area 326), and the eNB 330 has a coverage area 332 (dividedinto a center cell area 334 and a cell edge area 336). As shown, each ofthe eNBs 310-330 uses a different frequency (e.g., F1, F2, and F3) fortheir respective cell edge areas, which helps to reduce ICI in thenetwork 300. Notably, in some embodiments (e.g., soft FFR schemes), whenone of the eNBs 310-330 uses a particular frequency for cell-edgetransmissions, neighboring ones of the eNBs 310-330 may constrain theiruse of that frequency to low power transmissions. This may effectivelymitigate the inter-cell-interference in the network 300, while stillallowing the neighboring eNBs 310-330 to have at least limited use ofthat frequency.

Although useful for reducing ICI, static FFR schemes (such as thatillustrated in FIG. 3) underutilize bandwidth resources becausefrequency allocations do not adapt to different traffic patterns and/ornon-uniform user distributions amongst the various cells of the network.For instance, it may be advantageous for the eNB 310 to utilize both F1and F2 for cell-edge transmissions (e.g., while the eNB 320 and 330 usedifferent subbands of the F3 spectrum) if a disproportionately highnumber of UEs are positioned within the outer cell area 316 (incomparison to the outer-cell areas 326 and 336). As such, techniques fordynamically allocating frequency bands (or sub-bands) in networks usingFFR power schemes are desired.

Another alternative is centralized power control, where a global PCsolution is dynamically computed by a centralized PC controller. FIG. 4illustrates a wireless network 400 comprising a PC controller 401, a PCand a plurality of eNBs 410, 420, and 430 corresponding to a pluralityof coverage areas 412, 422, and 432 (respectively). The PC controller401 may be any device capable of computing PC solutions for the wirelessnetwork 400 based on data provided by the eNBs 410-430 and/or otherconfiguration data. The eNBs 410-430 may be configured similar to theeNBs 110-130 and/or the UEs 410-430, and may serve a plurality of UEs(which are not depicted in FIG. 4 for purposes of clarity andconcision).

The centralized controller 401 may dynamically compute PC solutions foreach of the eNBs 410-430 based on the data (e.g., channel statistics)provided by the eNBs 410-430. When using an exhaustive search approach,the PC controller 401 may consider all possible combinations of PCsolutions, and therefore may generate a set of PC solutions thatoptimizes throughput/coverage in the network 400. However (for reasonsdiscussed below), computing a global PC solution in a truly dynamicfashion using an exhaustive search approach may be difficult to executefrom a practical standpoint due to the finite computation resources inmany wireless networks. FIG. 5 illustrates a global PC solution 500 forthe eNBs 410-430. As shown, the global power solution 500 comprises a PCsolution 510 having a set of power settings and scheduling assignments[(p₁′,s₁′); . . . (p₁₆′,s₁₆′)] for the eNB 410, a PC solution 520 havingpower settings and scheduling assignments [(p₁″,s₁″); . . . (p₁₆″,s₁₆″)]for the eNB 420, and a PC solution 530 having power settings andscheduling assignments [(p₁′″,s₁′″); . . . (p₁₆′″,s₁₆′″)] for the eNB430. Each of the PC solutions 510-530 may be similar to the PC solution200, but may be computed collectively such that power settings andscheduling assignments in the PC solutions 520-530 are considered whendeveloping the PC solution 510 (and vice-versa).

Although dynamic computation of a global PC solution may (theoretically)optimize throughput/coverage, practical limitations may restrict itsimplementation in many networks. Specifically, the exhaustive searchapproach required to obtain optimal throughput/coverage may place aheavy computational load on the PC controller 401. This may beespecially problematic in large networks, where the computational loadmay become so onerous that dynamic globalized PC using an exhaustivesearch approach becomes infeasible.

One technique for reducing the computation load of the PC controller isto use a non-exhaustive algorithm (e.g., an algorithm that considersfewer than all possible combinations). However, non-exhaustivealgorithms that produce near-optimal results often-times cannot beperformed quick enough to achieve dynamic implementation, whilenon-exhaustive algorithms capable of achieving dynamic implementationoften produce sub-optimal results.

Another technique for reducing the computation load of the PC controlleris to delegate scheduling (e.g., including modulation and codingselection) to the eNBs 410-430. Specifically, computation of the PCsolution 500 can be bi-furcated into (essentially) two tasks, namely:(1) computation of a power pattern (e.g., a set of power settings forthe various time-frequency resources); and (2) scheduling. Inembodiments, scheduling may include assigning various time-frequencyresources to the UEs as well as selecting modulation and coding schemes(MCSs) for transmission). FIG. 6( a) illustrates a bifurcated PCsolution 600 comprising a set of power settings 612, 622, 632 and amatching set of scheduling assignments 614, 624, and 634, whichcorrespond to the PC solutions 510-530. Hence, when a hybrid approach isused, the power controller 401 is responsible for the power settings612, 622, 632 (i.e., joint power control (JPC)), while the eNBs 410-430are responsible for the scheduling assignments 614, 624, and 634 (i.e.,localized scheduling). The localized scheduling may include themodulation and coding (MCS) levels for those transmissions. Notably, thescheduling assignments 614, 624, and 634 are computed based on the powersettings 612, 622, 632, and hence localized scheduling may generally beperformed after global computation of the power control settings (i.e.,after JPC).

For purposes of clarity, corresponding power settings (e.g., p1′, p1″,p1′″) in the set of power patterns 612, 622, 632 can be represented asvectors, as depicted by the power pattern 652 in FIG. 6( b). As shown,each RB of the power pattern 652 has a power setting vector ({rightarrow over (p)}_(l)), which corresponds to the set of power settings(p_(l)′, p_(l)″, p_(l)′″) from the power patterns 612, 622, 632. Hence,the power pattern 652 is short-form notation of the power patterns 612,622, 632, from individual eNBs and likewise may be dynamically computed(e.g., according to algorithm) based on channel statistics reported fromthe eNBs 410-430.

FIG. 7 illustrates a protocol diagram depicting a prior artcommunication sequence 700 between the eNBs 410-430 and the PCcontroller 401 for the purpose of computing a power pattern 652. Thecommunication sequence 700 may begin with the eNBs 410-430 reportingshort-term channel statistics 705 to the PC controller 401. The channelstatistics 705 may specify the information gathered during thescheduling (S0), and hence may only reflect a snapshot of the trafficpattern and/or user distribution of the network 400. The PC controller401 may compute a power pattern (P1) upon receiving the channelstatistics 705, and thereafter return a set of corresponding powersettings 710 to the eNBs 410-430. Upon receiving the power settings 710,the eNBs 410-430 may perform a new round of scheduling (S1).Subsequently, the eNBs 410-430 will collect and report a second set ofchannel statistics 715 to the PC controller 401, which will be used tocompute a second power pattern (p2). Hence, a new power pattern (PN) iscomputed for each round of scheduling (SN).

As shown, the computation period (T_(c)) required to compute the newpower pattern may contribute to the overall period (T_(p)) required todynamically update the global PC solution in the network 400. As theperiod (T_(p)) lengthens, the network 400 becomes less nimble inadapting to changing traffic conditions and/or user distributions. Forinstance, if the period (T_(p)) is 1000 timeslots (e.g., T1, T2, . . .T1000), then the power pattern 652 may be updated relatively seldom,resulting in latencies and/or sub-standard performance. Hence therelative effectiveness of dynamic computation in the network 400 may belimited by the delay period (T_(p)) between updates, which mayincrease/lengthen as the network 400 grows larger and larger. Since moreaccurate non-exhaustive algorithms for computing the PC patterntypically require more processing resources and/or longer computationperiods (T_(c)), the relative effectiveness of said algorithms may belimited due to their inherently longer latencies (i.e., longer T_(p)).

Aspects of this disclosure describe a static approach, a semi-staticapproach, and an adaptive approach for computing power patterns, each ofwhich offer numerous advantages over the distributed/localized,static-FFR, and centralized/dynamic approaches discussed above. Thestatic approach for computing power patterns may comprise finding thosepower patterns that provide the best average performance for typicaltraffic conditions in the network. In some embodiments, the staticapproach may optimize power and frequency bands to achieve the bestoverall system performance, thereby offering significant advantages overthe static-FFR approach discussed above. The semi-static approach forcomputing power patterns may exploit the tendency of networks to observerepetitive and/or correlated scheduling scenarios (e.g., trafficpatterns, user distributions, etc.) during like time periods. Suchrepetitive and/or correlated scheduling scenarios may be attributable tothe tendency of users (as a whole) to behave in a predictable mannerduring like periods. For instance, scheduling scenarios (e.g., trafficpatterns, etc.) in a network or group of neighboring coverage areas maybe relatively similar at a certain time interval (e.g., 10 am-11 am) oneach day of the week. Hence, channel statistics gathered during a firstinstance of a time interval (e.g., 1 pm-2 pm on Monday) may becorrelative to channel statistics gathered during successivecorresponding intervals (e.g., Tuesday-Friday).

FIG. 8 illustrates a method 800 for computing power patterns for use insemi-static JPC with local scheduling. The method 800 begins at block805, where N is initialized to 0. Next, the method 800 proceeds to step810, where the PC controller 401 receives long-term channel staticscorresponding to an interval-N from the eNBs 410-430. The long-termchannel statistics may be collected over an extended period of time bythe eNBs 410-430 (e.g., one hour, etc.), and may correspond to aspecific period of time uniquely associated with interval-N (e.g.,between 12 pm and 1 am on weekdays). Next the method 800 may proceed toblock 820, wherein the PC controller 401 may generate a set of potentialpower patterns for a set of likely scheduling scenarios. The likelyscheduling scenarios may correspond to scheduling scenarios (e.g.,traffic patterns, etc.) observed during the period (i.e., theinterval-N) over which the long term channel statistics were collected.

Before carrying out this step, the controller may introducefast/temporal fading components to the long-term channel statistics, andthereafter generate additional scheduling scenarios based off themodified long term-channel statistics. The fading components may begenerated using information received from the distributed nodes, such asfading statistics of each channel (e.g., for each air channel between auser and each eNB). Various techniques exist for obtaining the fadingcomponents, such as Raleigh fading, Rician fading, etc. Adding temporalfading components may produce additional scheduling scenarios, and hencemay cause more power patterns to be computed (e.g., a power patter foreach additional scheduling scenario).

Next the method 800 may proceed to step 830, where the PC controller 401may shrink the set of potential power patterns into a sub-set of powerpatterns using a statistical narrowing technique. In other words, the PCcontroller 401 may use statistical narrowing techniques to removecertain power patterns (e.g., uncommon power patterns) and/or mergemultiple power patterns (e.g., redundant/correlated patterns) from theoriginal set of potential power patterns, thereby generating a sub-setof common power patterns. These common power patterns may be thosepatterns that provide near-optimal settings for a high percentage of thelikely (e.g., observed) scheduling scenarios (e.g., geographical trafficand user distributions, availability of other networks, interferencefrom other networks, etc.). Various narrowing techniques (discussed ingreater detail below) may be used to shrink the set of power-patternsinto the sub-set of desirable power patterns. For instance, onestatistical narrowing technique may merge repetitive and/orsubstantially correlated power patterns to identify high-probabilitypower patterns as well as low-probability power patterns. The same orother techniques may cull (i.e., remove) improbable power patterns,thereby creating a sub-set of power patterns that are likely to besuitable for the vast number of scheduling scenarios (e.g., trafficpatterns, user distributions, etc.) observed during the interval-N.

After shrinking the set of power patterns into the subset of commonpower patterns, the method 800 may proceed to step 840, where the PCcontroller may select one or more power patterns in the sub-set of powerpatterns to implement in the network during a future instance of theinterval-N. In some embodiments, the power patterns may be selectedrandomly. In other embodiments, the power patterns may be selected basedon an assigned probability (discussed below). The method 800 may thenproceed to step 850, where the selected power patterns may be sent tothe eNBs for implementation during the next periodic instance ofInterval-N. Subsequently, the method 800 may proceed to step 860 wherePC controller may increment N (e.g., by one). The method 800 may thenrepeat steps 810-860 until the power pattern(s) have been computed forall intervals in the cyclical period (e.g., until N>N_(max)), at whichpoint N will be re-initialized to zero such that the method 800 may berepeated for the next cycle of intervals (as N is incremented from 0 toN_(Max)). For instance, N_(max) may be equal to 23 during embodimentsusing 24 one-hour intervals for each day of the week.

FIG. 9 illustrates a cyclical sequence of intervals 900 comprising aplurality of intervals (I₀-I₂₃) that correspond to hours in a day (e.g.,I₀≈12 pm-1 am, etc.). As shown, the variables (e.g., channel statistics,traffic patterns, user distributions, etc.) of an earlier instance of agiven interval (I_(N)) are predicative of later instances of thatinterval.

FIG. 10 illustrates a protocol diagram depicting a communicationsequence 1000 between the eNBs 410-430 and the PC controller 401 for thepurpose of computing a power pattern 652. As shown, the communicationsequence 1000 begins with the eNBs 410-430 reporting of long termchannel statistics 1005 to the PC controller 401. The long term channelstatistics 1005 correspond to channel statistics collected during thescheduling (SN₀) occurring over a first instance of a given interval(I_(N) on Monday), and include more information (e.g., statisticallysignificant information) than the short term channel statistics 705discussed above. Notably, while days of the week (e.g., Mon, Tue, . . .Fri) are used herein as an exemplary embodiment, aspects of thisdisclosure may be applied to other cyclical time periods (e.g., schoolor office operating schedules, months, years) during whichrepetitive/predictive traffic patterns are observed. After receiving thelong-term channel statistics, the PC controller 401 may compute one ormore power patterns 1010 (e.g., P*, P**, etc.), which may becommunicated to the eNBs 410-430. The power patterns 1010 may becomputed by the PC controller 401 off-line, and may be implementedduring a second instance of the given interval (I_(N)) on Tuesday.Importantly, the scheduling SN₁ (on Tuesday) is performed by the eNBs410-430 based on the power patterns 1010, hence scheduling may beperformed in a dynamically and in distributed/localized fashion. Afterthe interval I_(N) on Tuesday has expired, the eNBs 410-430 may reportlong term channel statistics 1115 collected during the interval I_(N)(on Tuesday) to the PC controller 401. These exchanges may be repeateduntil Friday. In some embodiments, usage may tend to differsubstantially over the weekend, and hence channel statistics collectedon Monday-Friday may not be well-suited for predicting traffic patternsand/or user distributions on Saturday and Sunday. In such embodiments,statistics collected on a previous weekend may be used to compute powerpatterns to be used for semi-static scheduling on Saturday/Sunday.

The communication sequence 1000 may be advantageous over the prior artcommunication sequence 700 in several respects. For instance, the powerpatterns 1010, 1020 are based on long-term channel statics 1005, 1015(respectively), while the power patterns 705, 715 are typically based onshort term channel statistics 710, 715. As such, the power patterns1010, 1020 may provide (on average) better coverage/throughput thanpower patterns 705, 715, particularly when the lag (e.g., T_(P) in FIG.7) prevents the network from reacting swiftly to changes in usage.Further, the power patterns 1005, 1015 are computed offline (e.g., overa longer period of time), and therefore may consume fewer processingresources than computation of the power patterns 705, 715 (which aretypically computed dynamically in a quasi-real-time fashion). Withoutstringent time constraints, the power patterns 1005, 1015 may becomputed using a more thorough algorithm (e.g., an exhaustive approachor one has fewer compensations). A further advantage is that lesssignaling is performed in the communication sequence 1000 than in theprior art communication sequence 700, and therefore fewer networkresources are consumed.

In some embodiments, the method 800 may generate one power pattern(e.g., P*) that may be used for the entire interval (I_(N)). FIG. 11illustrates such an embodiment, where the power pattern P* is repeatedfor each timeslot (T1, T2, . . . TN) of the interval (I₀). In otherembodiments, the method 800 may generate several power patterns (e.g.,P*, P**, P***, etc.) that may be used over the entire interval (I_(N)).FIG. 12 illustrates such an embodiment, where a sequence of powerpatterns (P*, P**, and P***) are repeated over a consecutive timeslots(T1, T2, T3, . . . T[N−2], T[N−1], TN) of the interval (I₀). Althoughthe sequence of power patterns (P*, P**, and P***) are depicted as beingrepeated sequentially, non-sequential arrangements may be used in someembodiments (e.g., P*, P**, P*, P***, etc.).

The statistical narrowing techniques used to shrink the set of potentialpatterns into the sub-set of likely power patterns may vary depending onthe embodiment, and may include one or more of the following steps. Onenarrowing step may be to merge redundant and/or correlated powerpatterns. During merging process, the power levels of two patterns arecombined to form a single power pattern. In one embodiment, merging maybe achieved by deleting/removing the less probable power pattern withoutaltering the power level of the more probable power pattern. In otherembodiments, merging may be achieved by combining the correspondingpower levels using their probabilities as weights, e.g.,(a1*P1+a2*P2)/(a1+a2), and a2*P2, where a1 and a2 are the probabilitiesof each power pattern. Alternative techniques for merging power patternsmay also be used. Redundant power patterns may be those patterns havingidentical power settings (i.e., p1*=p1**; p2*=p2**, etc.). Correlatedpower patterns may be patterns that have similar power settings (e.g.,p1*≈p1**; p2**≈p2**; etc.). In embodiments, power patterns that aresubstantially correlated may be those have a Euclidian distance lessthan a threshold (E_(th)). For instance, a first power pattern (P*) anda second power pattern (P**) may be substantially correlated if theirEuclidian distance (E(P*, P**)) is less than the threshold (e.g., E(P*,P**)<E_(th)). The Euclidian distance of a given pair of power patterns(P*, P**) may be found according to the following formula:

E(P*,P**)=√{square root over ((p ₁ *−p ₁**)²+(p ₂ *−p ₂**)²+ . . . (p_(m) *−p _(m)**)²)}{square root over ((p ₁ *−p ₁**)²+(p ₂ *−p ₂**)²+ . .. (p _(m) *−p _(m)**)²)}{square root over ((p ₁ *−p ₁**)²+(p ₂ *−p₂**)²+ . . . (p _(m) *−p _(m)**)²)}

where p_(l) is a power setting for frequency band (f_(l)) and m is thenumber of frequency bands in the downlink channel.

Another narrowing step may be to remove improbable power patterns.Probability may be assigned based on the number of times a pattern wasmerged. For instance, there are 500 potential power patterns in the setof potential power patterns, then each power pattern (initially) has aprobabilistic weight of about 0.2%. Hence, merging fiveredundant/correlated power patterns into a single power pattern willreduce the set of potential power patterns to about 496, as well asallocate a probalistic weight to the merged power pattern of about 1%(e.g., 0.2% multiplied by 5).

Another narrowing step may be to rank the power patterns (e.g., aftermerging) based on their probability, and (subsequently) merge lessprobable power patterns. For instance, the more probable power patternmay absorb the less probable power patterns until enough improbablepower patterns have been culled. The utilized narrowing technique mayinclude one or more of the above discussed steps.

An adaptive dynamic approach to power pattern computation is analternative to the semi-static approach (FIG. 8) which may proveadvantageous in networks that have the ability to detect trafficpatterns, but lack the computational capacity to compute power patternson a regular basis. FIG. 13 illustrates a method 1300 for generating atable to use during the adaptive approach.

The method 1300 begins at step 1310, where an external processing devicemay receive long-term channel statistics collected in a network over anextended period of time. The method 1300 may proceed to step 1320, wherethe external processing device may identify a plurality of commonscheduling scenarios based on traffic patterns and/or user distributionsobserved over the extended period. The method 1300 may then proceed tostep 1330, where the external processing device may compute an optimalpower pattern for each common scheduling scenario based on the long termchannel statistics. The method 1300 may then proceed to step 1340, wherethe external processing device may build a table associating the optimalpower patterns with the common scheduling scenarios. The table may thenbe provided to a centralized device (e.g., the PC controller 401) or toone or more distributed devices (e.g., the eNBs 410-430) for use duringimplementations of the adaptive FFR scheme.

In some embodiments, adaptive scheduling and MCS may be performedlocally. For instance, the table could be provided to a plurality ofdistributed eNBs with an instruction to begin adaptive scheduling at acertain time. Hence, each cell could perform scheduling and MCSadaptation locally with the knowledge of their neighbors' power levelsand the associated measurements. If the repetitive number of patterns(Nr) is a multiplication of the number of resource units to be scheduledin a given time slot, these interference signal measurements wouldlikely be relatively accurate.

Experimentation and analysis has shown that a relatively low number ofscenarios could be used to represent different scheduling scenarios inthe system. An additional observation is that when the system has arelatively light load, optimal schemes may not be necessary. As thesystem experiences new scheduling scenarios (e.g., scheduling scenariosthat differ significantly from those existing in the tables), the newscheduling scenarios could be sent to a central device such that acorresponding table entry can be added. Until an updated adaptivescheduling table comprising the new table entry is available the, theclosest scheduling scenario in the existing adaptive scheduling tablecould be used. In some embodiment, the introduction of a new cell wouldnecessitate the creation/preparation of an updated adaptive schedulingtable.

FIG. 14 illustrates a method 1400 for implementing an adaptive FFRscheme (e.g., based on the table provided by the method 1300). Themethod 1400 may begin at step 1410, where a network device may detect acommon scheduling scenario based on channel statistics. Next, the method1400 may proceed to step 1420, where the network device may identify anoptimal power pattern associated with the common scheduling scenario byreferencing the adaptive FFR table. Next, the method 1400 may proceed tothe step 1430, where the network device may implement the identifiedpower pattern. After a delay 1440, the method 1400 may repeat the steps1410-1430. In some embodiments, the method 1400 may be performed by acentralized device (e.g., PC controller 401). In other embodiments, themethod 1400 may be performed by one or more distributed devices (e.g.,eNBs 410-430). In some embodiments, a determination may be made toupdate the adaptive scheduling table. For instance, such a determinationmay be made periodically, such as between steps 1440 and 1410 (or otherbetween other steps in the method 1400). This determination may betriggered by the occurrence (or non-occurrence) of some event, such asdetection of a new scheduling scenario occurs (e.g., a schedulingscenario that does not have a corresponding entry in the presentadaptive scheduling table), the addition (or removal) of a neighboringcell, etc.

FIG. 15 illustrates coverage/throughput performance of various powercontrol schemes obtained through simulation. In this example, varioussimulations were performed for a network comprising 57 cells toillustrate the potential performance advantages that may be achievedthrough implementation of one or more aspects of this disclosure. InFIG. 15, the reference point labeled no PC identifies results obtainedfrom a simulation performed without any coordinated power control, i.e.all nodes use same transmit power for all the transmissions, thetransmit power is set to the maximum transmit power that can be used bya cell. The reference point labeled DYNAMIC JPC identifies resultsobtained from a simulation performed using a dynamic joint power controland scheduling scheme, which assumes the power and scheduling decisionsof all cells are made by the central controller for each time slot andresource unit. It should be noted that, in practice, dynamic joint powercontrol and scheduling (e.g., using an exhaustive search approach) mustgenerally include a number of concessions (e.g., simplifications) whichaffect the accuracy of the results (e.g., prevent the algorithm fromidentifying the truly optimal solution). Consequently, dynamic JPC andscheduling schemes (i.e., which include some concessions) often do notperform as well as other PC schemes. Notably, the reference pointlabeled DYNAMIC JPC represents the best performance that could beobtained from real-world dynamic JPC and scheduling schemes (which, bytheir very nature, include at least some concessions). The referencepoint labeled DYNAMIC JPC+LOCAL PF identifies results obtained from asimulation of a system that employed dynamic power control with localscheduling. The reference points labeled (N0, Nr) identify resultsobtained according to embodiments of the semi-static PC scheme disclosedabove. Notably, the simulations were performed under the assumption thatthe user distributions were the same as those used generate the powerpatterns. Under this assumption, we can see that the use of repetitiveschemes with fewer repetitive patterns provide near optimal performance.For example, the use of N0=200 and a repetitive pattern of 50 offersslightly better performance than that obtained through joint powercontrol and localized scheduling. It should also be noted that having arepetitive pattern of 50 means that 50 frequency blocks for a given timeslot are each assigned a power pattern that is maintained throughout thesimulation. Since power of the neighbors remain the same during thisfrequency block for all the time slots, the interference would remainthe same and MCS adaptation would be more accurate. Notably, the numberof repetitive patterns (Nr) is an integer multiple of the number ofresource blocks (Nrb) in a single time slot. As such, the interferencereceived in a given RB repeats in a cyclical manner, thereby makinginterference measurements and MCS adaptation more accurate.

FIG. 16 illustrates a block diagram of an eNB 1600. The base station1600 may include a PC controller interface 1602, a processor 1604, amemory 1605, a transmitter 1606, a receiver 1608, a coupler 1610, and anantenna 1612, which may be arranged as shown in FIG. 16. The PCcontroller interface 1602 may be any component or collection ofcomponents that allows the eNB 1600 to engage in network communicationswith a PC controller. The processor 1604 may be any component capable ofperforming computations and/or other processing related tasks, and thememory 1605 may be any component capable of storing programming and/orinstructions for the processor. The transmitter 1606 may be anycomponent capable of transmitting a signal, while the receiver 1608 maybe any component capable of receiving a signal. The coupler 1610 may beany component capable of isolating a transmission signal from areception signal, such as a duplexer. The antenna 1612 may be anycomponent capable of emitting and/or receiving a wireless signal. In anembodiment, the eNB 1600 may be configured to operate in an LTE networkusing an OFDMA downlink channel divided into multiple subbands orsubcarriers and using SC-FDMA in the downlink. In alternativeembodiments, other systems, network types and transmission schemes canbe used, for example, 1XEV-DO, IEEE 802.11, IEEE 802.15 and IEEE 802.16,etc.

FIG. 17 illustrates a block diagram of an embodiment PC controller 1700.The PC controller 1700 may include a base station (BS) interface 1702, aprocessor 1704, a memory 1705, and a PC controller interface 1708, whichmay be arranged as shown in FIG. 17. The BS interface 1702 may be anycomponent or collection of components that allows the PC controller 1700to engage in network communications with a BS. The processor 1704 may beany component capable of performing computations and/or other processingrelated tasks, and the memory 1705 may be any component capable ofstoring programming and/or instructions for the processor. The PCcontroller interface 1708 may be any component or collection ofcomponents that allows the PC controller 1700 to engage in networkcommunications with other PC controllers.

Although the present invention and its advantages have been described indetail, it should be understood that various changes, substitutions andalterations can be made herein without departing from the spirit andscope of the invention as defined by the appended claims. Moreover, thescope of the present application is not intended to be limited to theparticular embodiments of the process, machine, manufacture, compositionof matter, means, methods and steps described in the specification. Asone of ordinary skill in the art will readily appreciate from thedisclosure of the present invention, processes, machines, manufacture,compositions of matter, means, methods, or steps, presently existing orlater to be developed, that perform substantially the same function orachieve substantially the same result as the corresponding embodimentsdescribed herein may be utilized according to the present invention.Accordingly, the appended claims are intended to include within theirscope such processes, machines, manufacture, compositions of matter,means, methods, or steps.

What is claimed is:
 1. A method for providing joint power control (PC)and scheduling in a wireless network, the method comprising: receiving,by a controller, long term channel statistics corresponding to downlinkchannels extending from a plurality of base stations to a plurality ofuser equipments (UEs), wherein the long term channel statistics arecollected during an instance of a periodic time interval; identifying aplurality of scheduling scenarios that are likely to occur during theperiodic time interval; generating a set of possible power patterns inaccordance with the long term channel statistics, wherein each possiblepower pattern in the set of possible power patterns provides an optimalsolution for a unique one of the plurality of scheduling scenarios;narrowing the set of possible power patterns into a sub-set of commonlyused power patterns; and selecting one or more power patterns from theset of commonly used power patterns, wherein the one or more selectedpower patterns are implemented by the plurality of base stations duringa second instance of the periodic time interval.
 2. The method of claim1, wherein narrowing the set of possible power patterns into the sub-setof commonly used power patterns comprises: merging redundant powerpatterns in the set of possible power patterns to generate anon-redundant set of power patterns.
 3. The method of claim 1, whereinnarrowing the plurality of possible power patterns into the set ofcommonly used power patterns further comprises: merging correlated pairsof power patterns in the set of possible power patterns to generate anon-homogenous set of power patterns.
 4. The method of claim 3, whereinmerging correlated pairs of power patterns in the set of possible powerpatterns comprises: determining a correlation coefficient for eachcombination of possible power patterns in the set of possible powerpatterns; and merging those combinations of power patterns having acorrelation coefficient that is less than a threshold.
 5. The method ofclaim 4, wherein determining a correlation coefficient for eachcombination of possible power patterns in the set of possible powerpatterns comprises computing a Euclidian distance in accordance with thefollowing formula: E(P*,P**)=√{square root over((p₁*−p₁**)²+(p₂*−p₂**)²+ . . . (p_(m)*−p_(m)**)²)}{square root over((p₁*−p₁**)²+(p₂*−p₂**)²+ . . . (p_(m)*−p_(m)**)²)}{square root over((p₁*−p₁**)²+(p₂*−p₂**)²+ . . . (p_(m)*−p_(m)**)²)} where E(P*, P**) isthe Euclidian distance for a given combination of a first power pattern(P*) and a second power pattern (P**), p_(i)* is a power setting forfrequency band (f) of P*, p_(i)** is a power setting for frequency band(f_(i)) of P**, and m is the number of frequency bands in the downlinkchannel.
 6. The method of claim 3, wherein narrowing the set of possiblepower patterns into the set of commonly used power patterns furthercomprises: assigning probabilities to each power pattern in thenon-homogenous set of power patterns in accordance with a number ofpower patterns that were merged to obtain that particular power pattern;identifying two or more least-probable power patterns in thenon-homogenous set of power patterns; and merging the two or moreleast-probable power patterns.
 7. The method of claim 3, whereinnarrowing the plurality of possible power patterns into the set ofcommonly used power patterns further comprises: assigning probabilitiesto each power pattern in the non-homogenous set of power patterns inaccordance with a number of power patterns that were merged to obtainthat particular power pattern; and removing one or more least-probablepower patterns in the non-homogenous set of power patterns, therebyforming the set of commonly used power patterns.
 8. The method of claim1, wherein the one or more selected power patterns include at least twopower patterns that are implemented in a repetitious manner during asecond instance of the periodic time interval.
 9. The method of claim 1,wherein local scheduling is performed in the wireless network inaccordance with the one or more selected power patterns.
 10. The methodof claim 1, wherein identifying the plurality of scheduling scenariosthat are likely to occur during the periodic time interval comprises:identifying a first set of scheduling scenarios in accordance with thelong term channel statistics; modifying the long term channel statisticsby adding fast/temporal fading components to the long-term channelstatistics; identifying a second set of scheduling scenarios inaccordance with the long term channel statistics; and identifying theplurality of scheduling scenarios as including both the first set ofscheduling scenarios and the second set of scheduling scenarios.
 11. Acentralized controller in a wireless network, comprising: a processor;and a non-transitory computer readable storage medium storingprogramming for execution by the processor, the programming includinginstructions to: receive long term channel statistics collected during afirst instance of a periodic time interval; identify a plurality ofscheduling scenarios at least some of which occurring during the firstinstance of the periodic time interval; generate a set of possible powerpatterns in accordance with the long term channel statistics, whereineach possible power pattern in the set of possible power patternsprovides an optimal solution for a unique one of the plurality ofscheduling scenarios; narrow the set of possible power patterns into aset of commonly used power patterns; and select one or more powerpatterns from the set of commonly used power patterns, wherein the oneor more selected power patterns are implemented in the network during asecond instance of the periodic time interval.
 12. The centralizedcontroller of claim 11, wherein the instructions to narrow the set ofpossible power patterns into the set of commonly used power patternsincludes instructions to: merge similar power patterns in the set ofpossible power patterns with one another to generate a non-homogenousset of power patterns; assign probabilities to each power pattern in thenon-homogenous set of power patterns in accordance with a number ofpower patterns that were merged to obtain that particular power pattern;and remove least probable power patterns from the non-homogenous set ofpower patterns to form the set of commonly used power patterns.
 13. Thecentralized controller of claim 11, wherein the one or more selectedpower patterns include at least two power patterns that are implementedin a repetitious manner during the a second instance of the periodictime interval.
 14. The centralized controller of claim 11, wherein localscheduling is performed in the wireless network in accordance with theone or more selected power patterns.
 15. A method for facilitatingdynamic adaptive fractional frequency reuse (FFR) in a wireless network,the method comprising: receiving long term channel statistics obtainedover an extended period in the wireless network; identifying a pluralityof common scheduling scenarios observed during the extended period;generating a table of optimal power patterns in accordance with the longterm channel statistics, wherein the table of optimal power patternscomprises a unique power pattern for each of the plurality of commonscheduling scenarios; and providing the table of optimal power patternsto one or more devices in the network, wherein the table of optimalpower patterns is used by the one or more devices to dynamically selectpower patterns in accordance with detected scheduling scenarios in thewireless network.
 16. The method of claim 15, wherein the plurality ofcommon scheduling scenarios comprise user distributions and trafficpatterns that are likely to occur in the wireless network during theextended period.
 17. The method of claim 15, wherein the one or moredevices include a plurality of base stations, and wherein the table ofoptimal power patterns is used by each of the plurality of base stationsto independently select power patterns in a distributed fashion.
 18. Themethod of claim 15, wherein one or more devices include a centralizedcontroller, and wherein the table of optimal power patterns is used bythe centralized controller to globally select power patterns for aplurality of base stations.
 19. An apparatus comprising: a processor;and a computer readable storage medium storing programming for executionby the processor, the programming including instructions to: receivelong term channel statistics obtained over an extended period in awireless network; identify a plurality of common scheduling scenariosobserved during the extended period; and generate a table of optimalpower patterns in accordance with the long term channel statistics,wherein the table of optimal power patterns comprises a unique powerpattern for each of the plurality of common scheduling scenarios. 20.The apparatus of claim 19, wherein the programming further includesinstructions to: provide the table of optimal power patterns to one ormore devices in the wireless network, wherein the table of optimal powerpatterns is used by the one or more devices to dynamically select powerpatterns in accordance with detected scheduling scenarios in thewireless network.
 21. The apparatus of claim 20, wherein one or moredevices include a plurality of base stations, and wherein the table ofoptimal power patterns is used by each of the plurality of base stationsto independently select power patterns in a distributed fashion.
 22. Theapparatus of claim 20, wherein the one or more devices include acentralized controller, and wherein the table of optimal power patternsis used by the centralized controller to globally select power patternsfor a plurality of base stations.